Graduate Outcomes Measuring success and providing opportunity Welcome #GraduateOutcomes
Graduate OutcomesMeasuring success and providing opportunityWelcome
#GraduateOutcomes
Graduate Outcomes ConferenceAndy Youell, Director of Data Policy & Governance
History
• 1972 – First Destinations Record (USR)
• 1984 – Exam Results and First Destinations Supplement (DfE)
• 1993 – First Destinations Supplement (HESA)
• 2002 – Destinations of Leavers from HE (HESA)
• 2017 – Graduate Outcomes
Increasing significance…
• Policy, funding and regulation
• Information for HE providers
• Information for prospective students
• Information for current students
• National statistics
• League tables
Reforming the HE data infrastructure
• Graduate Outcomes
• Data Futures
• Data Landscape Steering Group
Aims of today
• Where we are now
• HESA implementation of Graduate Outcomes
• Provider responsibilities and opportunities
• Different perspectives on Graduate Outcomes
• Looking to the future
• Networking and discussion
Graduate OutcomesMeasuring success and providing opportunity
#GraduateOutcomes
From DLHE to Graduate Outcomes
Dan Cook, Head of Data Policy & Development
The remit
WorkingStrategic
Review groups
Events
Research reports
Conference
Articles and blogs
International interest and engagement
Two consultations
With two rounds of feedback
Tackled difficult issues
Proposed novel solutions
Implementing the model
How will it operate?
Implementation timeline
Materials currently available
• Information for students
• Guidance on roles and responsibilities of HE providers
• Promotional materials and brand identity
• Record specification for the collection of graduates’ contact details
• Steering Group in operation
• Data protection guidance
Progress (1)
• Survey has been cognitively tested with graduates and published
• Engagement strategy under development
• Running survey through Confirmit system
• Procuring a call centre and supplier to conduct coding
• Developing HESA systems for providers to:− Upload contact details− Personalise the survey (including provider logo)− Access survey data dashboards
Progress (2)
• Design and develop detailed plans for outputs
• Detailed analysis and development of approach to using linked data
• Recruitment to roles at HESA to support survey operations
Graduate OutcomesMeasuring success and providing opportunity
#GraduateOutcomes
Standing up the capability
Doug Sparrow, Project Sponsor
Objectives
• High data quality
• Consistent quality
• Minimise risk
• Minimise complication for the sector and students
• Minimise the overall cost
Driving high quality
• Single integrated best of class survey platform
• Utilising a pre qualified and established framework for procuring the call centre
• Cognitive testing of the question banks
• Intended use of established and familiar SIC and SOC coding expertise
• Utilising the established HESA Liaison and Data Quality functions
Achieving consistent quality
• Split tasks to separate areas of expertise
• Each task applied evenly across all students
• Quality managed centrally
Minimising risk
• Contracted with industry leading companies
• Procuring the contact centre through an established framework
• Recruiting additional internal expertise
• Incorporating the expertise of ONS
Minimising complication
• Single and consistent point of contact for Higher Education Providers - HESA
• Development of a single portal
− Contact detail collection
− Personalisation
− Reporting
Minimising costs
• Procuring expertise not risk
• Structuring the collection and engagement to maximise online responses
• Building on, not replicating existing HESA capabilities
• Continuing to use the expertise throughout the sector to raise awareness
• Delaying the subscription point to 30 November
Graduate OutcomesMeasuring success and providing opportunity
#GraduateOutcomes
Understanding the questionnaire and opt-in
question banksRachel Hewitt, Data Policy & Governance Manager
How has the Graduate Outcomes survey been developed?
NewDLHE review – consultation one• What data should we collect?
NewDLHE review – consultation two• 84% support for survey design
Cognitive testing
Final version published• June 2018
Questionnaire: core
‘Traditional’ employment
One or more jobs
Full time/part
time
SOC information Supervision
Contract type
Salary SIC information
Qualification requirement
Job choice Length of employment
‘Non-traditional’ employment
Running your own business
Self-employment
Developing a creative/artistic/business portfolio
‘Non-traditional’ employment
Full time/part time SOC information Supervision SIC information
Qualification requirement Job choice Length of
employment
Company funding• Running your own
business
Salary• Self employment
Study, training or research
Full time/part time Qualification type University/college name
Location of study
Early destinations
Number of previous jobs
Number and type of
previous study
Additional measures
Graduate Voice measures• On track• Meaningful• Skills
Subjective wellbeing measures
Questionnaire: opt-in
What are opt-in question banks?
• Providers can ‘opt-in’ to having additional questions asked to core from a pre-defined set
• Additional cost – more information to follow on prices
• Some may be ‘opted-into’ by other bodies e.g. UKRI, NCTL
• Select opt-in banks through the provider portal
Opt-in banks
How did you find your job?
Net Promoter Score
Would you choose your
course again?
Would you like your careers
service to follow up?
Teacher training Research students
Cognitive testing
Why cognitively test?
To test:
• Understanding of the questions and corresponding response options and whether they are interpreted as intended
• Recall (of what they were doing in the census week) and the strategies they use to recall this information
• Judgement (exploring motivation, sensitivity and social desirability)
• Response (mapping the intended response with available options)
• 95 cognitive interviews
• LocationsLondonManchesterGlasgowCardiffBelfast
• Type of provider
• Personal characteristics
• Subject areas
• 1094 cognitive interviews
• Top level views provided on experience of completing survey
Online surveyWorkshops/Skype
Cognitive testing
Positives
• Graduate voice measures
• Flow
• Representation of variety of outcomes
• Census week
• Length of survey (47 seconds longer than DLHE)
• Survey name
Lessons learned
• Clarified activities
• Further developed self-employment/running a business
• Handling of those developing a portfolio/working a portfolio career
• Order survey based on most important activity
• Tightening up wording throughout
Next steps
Beyond the 2017/18 survey
• Continue to refine the survey
• No changes between cohorts
• Seeking expertise on developing a creative/artistic/professional portfolio questions
• Building in provider questions
Graduate OutcomesMeasuring success and providing opportunityCoffee break
#GraduateOutcomes
Promoting Graduate Outcomes: changes and challenges
Tammy Goldfeld, Head of the Careers Service
Dr Miriam Firth, Associate Director of Employability and Professional Learning
The University of Manchester:A Case Study
• Largest single-site university in the UK• Biggest student community: 40,490 students• @25% of UGs are WP
Strategy: Key Performance Indicator
By 2020 to achieve a positive destinations rate of at least 85% (as measured six months after graduation in the Destinations of Leavers from Higher Education Survey), ensuring that the University is ranked in the upper quartile of Russell Group institutions on this measure.
Widening Participation (WP)
Achieve a year-on-year increase in the % of WP students in positive employment destinations
and
narrow any gap between WP and non-WP students in achieving successful employability outcomes.
Strategy
New: Graduate Outcomes Strategy Group, to replace the outgoing DLHE Strategy Group.
Consists of members from Careers, Planning, Alumni and Faculties
Additional GO Implementation Group, includes IT Services
Practical, operational changes
• Outsourced the final DLHE collection for the first time• New careers events for graduates (Leeds, Manchester,
London)• Updates to Data Protection statements
New ambition and KPI Opportunity of better data and analytics – correlations with
demographics (eg WP); student engagement; career decision making; work experience, etc.
Closer relationships with new graduates
To conclude: Opportunities Ahead
Graduate OutcomesMeasuring success and providing opportunity
#GraduateOutcomes
Graduate Outcomes for Careers Services
Andrew Whitmore, Joint chair of AGCAS GEOD group, University of Manchester
Graduate Outcomes, HEP responsibilities...
• Collect and maintain contact details• Map contacts with graduates…who?
• Return contact details• Inform students, graduates and staff• Data protection compliance• Data monitoringWho will have responsibility for driving this at your institution?
The impact on Careers Services • Forging complementary relationships with colleagues, e.g.
Academic Planning and Alumni teams• Map contacts with graduates…who?
• Agreeing roles and responsibilities• Check HESA web pages for examples
• Developing new approaches to professional practice - E.g.• "Formal" post graduation support lengthening to15 months and
beyond • targeted support for second jobbers at 15 months and beyond
Working with Students and Graduates (1): Supporting students (Pre-exit)
• Informing them about the new survey• Encouraging them to update contact details• Encouraging them to complete the survey
Working with Students and Graduates (2): Supporting recent graduates
• A new kind of relationship• To beyond immediately after graduation e.g. at 15 months….• Incentivising engagement with Careers Services.
Working with Students and Graduates (3): Continuous communications & support – 15 months
• Post-PGT experience• Digital support• Access to vacancies• Physical support….
• access to guidance and training• access to CPD
Working with Colleagues (1): Informing them
• Senior Management• Planning/Alumni/Student Records• Academics• Data protection staff• Recruitment teams• Careers team
Working with Colleagues (2): Monitoring and Reporting
• HESA Dashboard• Monitoring responses• Marketing to non-respondents• Reporting within your institution
• Senior management• Planning teams• Marketing teams• Records team
Discussion
What are you doing/ planning to do….
• To inform students?• To engage, inform and support graduates?• To work with colleagues to ensure that accurate contact
information will be available?
Last thoughts…
• Check HESA pages• Get on the Graduate Outcomes Jiscmail group• AGCAS regional groups, check• Look out for messages from AGCAS GEOD (Graduate
Employability and Outcomes Data Group )
Graduate OutcomesMeasuring success and providing opportunity
#GraduateOutcomes
Methodological Considerations for the Graduate Outcomes Survey
Salah Merad
Methodology, Office for National Statistics
Outline
• Introduction
• Survey-process overview: the GSBPM• Data Collection
• Non-response and weighting
• Summary
Aim:
to provide some insights into some survey-methodology areas and their implications
83
Introductions
• Me, and my role at ONS and in the GSS
• The Graduate Outcomes Steering Group:• Role
• Input on methods
• National Statistics and the Code of Practice
84
Context
• Comparison with other official statistics:
Similarities with social surveys• Individual responses
• Clustered by course, HE provider
• Non-response
Similarities with business surveys• Surveys of organisations (HE providers)
• range of sizes; variety; contacts
• A lot of auxiliary information available on the frame
85
GSBPM
• The Generic Statistical Business Process Model• https://statswiki.unece.org/display/GSBPM/GSBPM+v5.0
• https://gss.civilservice.gov.uk/wp-content/uploads/2016/01/Generic-Statistical-Business-Process-Model.pdf
• Specify Needs >>> Design >>>
Build >>> Collect >>> Process
>>> Analyse >>> Disseminate
>>> Evaluate >>>
• Covers entire survey process86
GSBPM
Methodological areas include:
• sample design
• data collection
• data cleaning (editing, coding, imputation)
• weighting and estimation
• analysis methods, e.g. seasonal adjustment
• disclosure control
• dissemination
... we’ll look at two areas:
data collection and non-response 87
Data Collection
Changes in data collection method and mode:
• Self-administered, web-collection
• Interviewer-administered, phone interview
… leading to potentially different: responses, response levels and patterns, quality of data,
opportunities, help/support available, …, cost
88
Data Collection
Also different questions/topics, questionnaire layout, wording, ordering, routing, approach to coding (classifications).
But not starting afresh.
Lots of experience with DLHE and good practice.
Plus cognitive testing (what works, where are there problems, respondents’ interpretation of questions).
89
Data Collection
Also need to gain respondents’ co-operation.
Traditionally, part of interviewers’ role.
Wording, invitation to respond, advance communication.
Incentives (for respondents, for providers?)
… quality and cost-effectiveness.
90
Non-response
Not everyone responds.
Causes problems:
• smaller samples for analysis
• unrepresentative samples … non-response bias
NR-bias occurs if responses vary between different groups, and the propensity to respond is associated with the response itself.
91
Non-response
Types of missingness (assumptions required)
• Missing Completely at Random … no risk of bias, but unlikely to be realistic.
• Missing at Random … propensity to respond related to other, known characteristics (e.g. sex, age, location), but not the response itself.
Usual assumption in official statistics. Common practice is to impute for item non-response, and (re-)weight for unit non-response.
• Not Missing at Random … no obvious way to proceed.
92
Non-response
Greater risk of NR-bias as the response rate falls.
Some examples:
• Population Census (mandatory). 94% overall, > 80% in every local authority.
• Business surveys (mandatory). Still usually achieve 70-75% by number (and more by size).
• Social surveys (voluntary). Rates are falling, often now 50-60%.
93
94
0
10
20
30
40
50
60
70
80
90S
N97
SN
98
SN
99
SN
00
SN
01
SN
02
SN
03
SN
04
SN
05
JS06
JS07
JS08
JS09
JS10
JS11
JS12
JS13
JS14
JS15
JS16
JS17
Ho
use
hold
resp
ons
e ra
te (
%),
exc
l. im
put
ed
LFS wave-specific response rates, GB 1997-2018
Wave 1 Wave 2 Wave 3 Wave 4 Wave 5 Total
Non-response
Graduate Outcomes – reference date moving from 6 months to 15 months:
• more time to lose contact
• more time for survey to become less salient
General decline in survey response.
Response-rate targets are used, and need to be practical (will need to be set lower than for DLHE).
95
Non-response
Weighting will be introduced; responding sample made to represent both themselves and the non-responders:
• explicit non-response model?
• calibration to known population totals?
• alternatively, could consider unit imputation (would give complete dataset and help with small groups)?
Huge source of information available for the non-responders (compare with social surveys!), including linked administrative data, giving excellent scope for effective non-response weighting.
96
Non-response
Output statistics will come from use of the weights.
Result will be each responder has a weight, w >= 1; interpret was number of graduates represented.
(Note that GOS is still a census, with high response expected).
Sum of w will be a meaningful number.
Work now to determine the weighting classes/models,
and any measures of accuracy. 97
Conclusions
Graduate Outcomes seems in a good place, methodologically.
A big and rich data source, including for the non-responders.
New and changed methods ahead, which will bring some discontinuities (improvements).
Being well-tested, and well-managed by the steering group.
98
Graduate OutcomesMeasuring success and providing opportunityLunch
#GraduateOutcomes
Improving graduate earnings measures using
LEO data
Alison Judd
Higher Education Analysis
What is LEO data?The Longitudinal Education Outcomes database (LEO) brings together information on education, labour market, and benefits into a single, secure, linked database.
101
Childcare & Early Years data
School records
Further Education records
Higher Education records
Benefits dataEmployment –P14 & P45 records
What does LEO bring to HE data?• LEO brings together information on education, labour
market, and benefits into a single linked database.
• Changes current reliance on large scale surveys of graduates– Labour Force Survey– Destinations of Leavers from Higher Education (DLHE) – 6 months & 40 months
• Providing much better data on employment outomes:– More reliable earnings and employment information. The DLHE survey contains
earnings data for approx. 60% of HE leavers and LDLHE just under one-third. – Scale means able to look at smaller course and population groups– Longitudinal – beyond the current 3.5 years of DLHE– More information on prior attainment and background characteristics
Helping improve decision making• Improving student/ parent decision making:
– E.g. Unistats and other information sources– Teaching Excellence Framework.
• Improving policy making– Ensuring system delivers for students– Understanding vfm of different public investments– Supporting social mobility
• Helping providers understand student outcomes– What works?
• Supporting academic research and analysis
But its not perfect…
104
• Information on where an individual works is not currently available. Geography
• Information on hours worked is not available so it is not possible to identify if an individual works full or part time.
Hours worked
• The database does not include information on what job an individual holds. We are exploring whether we can obtain information on the industry an individual works in (SIC code).
Occupation
• It doesn’t contain everything we would like to know
As with other sources: • Gross outcomes can’t be interpreted as causal• It only covers labour market outcomes• Inevitably, it is backwards looking.
• 3 year development programme, following SBEE Act
• Initial set of six ‘experimental’ publications to broaden understanding and get user feedback
• Note: Experimental does not mean poor quality!
• Now includes self-employment, Further Education Colleges and International Students
• Working with information providers to see how it can be used alongside other data to improve student choice
• Included as a supplementary metric within TEF
How we are taking it forward
So what does it show…?
106
Employment outcomes of graduates one, three, five and ten years after graduation Coverage: UK domiciled male and female first degree graduates from English HEIs and FECsCohorts: 2004/05 (10 years after graduation), 2009/10 (5 years), 2011/12 (3 years), 2013/14 (1 year)Tax year: 2015/16 Source: https://www.gov.uk/government/statistics/graduate-outcomes-2015-to-2016
Employment by subject
107Proportion in sustained employment, further study or both by subject five years after graduationCoverage: UK domiciled male and female first degree graduates from English HEIs Cohorts: 2008/09 (5 years), Tax year: 2014/15. Source: https://www.gov.uk/government/statistics/graduate-outcomes-by-degree-subject-and-university
Outcomes similar, with small impact from incl. self-employment.
Earnings after graduation
108Earnings of graduates by sex one, three, five and ten years after graduationCoverage: UK domiciled male and female first degree graduates from English HEIs and FECsCohorts: 2004/05 (10 years after graduation), 2009/10 (5 years), 2011/12 (3 years), 2013/14 (1 year)Tax year: 2015/16. Source: https://www.gov.uk/government/statistics/graduate-outcomes-2015-to-2016
Earnings outcomes by subject (f)
109Earnings of graduates by subject five years after graduationCoverage: UK domiciled female first degree graduates from English HEIs and FECsCohorts: 2009/10 (5 years), Tax year: 2015/16. Source: https://www.gov.uk/government/statistics/graduate-outcomes-2015-to-2016
• Significant variation between and within different subjects.
110Earnings of graduates by subject five years after graduationCoverage: UK domiciled male first degree graduates from English HEIs and FECsCohorts: 2009/10 (5 years), Tax year: 2015/16. Source: https://www.gov.uk/government/statistics/graduate-outcomes-2015-to-2016
Earnings outcomes by subject (m)• Similar pattern for men, but greater within subject range.
Including self-employment data again makes little different to subject level comparisons
111Earnings by subject five years after graduationCoverage: UK domiciled male and female first degree graduates from English HEIs Cohorts: 2008/09 (5 years), Tax year: 2014/15. Source: https://www.gov.uk/government/statistics/graduate-outcomes-leo-including-self-employment-earnings-data
Course level outcomes (1)
112
We see significant variation across institutions for each subject e.g. yellow dots highlight all Business & Admin
113
Course level outcomes (2)We see significant variation across subjects for each institution e.g. yellow dots look at Southampton courses
Next Steps
114
Next publicationo 21st June 2018 : Update institution by subject data with the latest tax year.
Commissioned research to control for influencing factors
Working with information providerso Unistatso Which?o Open Data Challenge
Looking at how to enable wider (secure) access for research
Summary
115
• An important asset in understanding students’ labour market outcomes: a more accurate, representative and granular picture than ever before.
• Help understand variation by subject, institution and student characteristic.
• Aim to support students (and their parents), policy makers and institutions in making better decisions.
• But needs to be used carefully, in context and recognising wider influences on student decision making and value of Higher Education.
• As well as further developing the data, keen to work with students, providers, researchers and information providers to maximise its usefulness.
Graduate OutcomesMeasuring success and providing opportunity
#GraduateOutcomes
Provider Context
Government Context
Graduate Context
Impact
Making this work
Use of graduate data
• Alumni Office• Faculty• Marketing• Data analysis/Information provision /Research• Data linking• Government tracking
Data protection principles
• Fair, lawful and transparent• Specified, explicit purposes• Adequate, relevant and limited to only what is necessary• Accurate and, where necessary, kept up to date• Kept for no longer than is necessary• Secure
Managing transition /challenges
• Impact of the new survey on the results• Using all of the survey • Open data
Opportunity
• Success determined by the graduate
• Improved use of data to inform student choices
• Greater join up between Faculty and professional services
• Visible benefits to students before they graduate
Graduate OutcomesMeasuring success and providing opportunity
#GraduateOutcomes
Visualising Destinations and OutcomesGraduate Outcomes: Measuring success and providing opportunity – 13 and 14 June 2018
Janette Hillicks – Senior Co-Design Manager, Jisc
Presenters
Rhodri Rowlands - Senior Data Visualisation Officer, Jisc
1. Quick overview of Analytics Labs and Community Dashboards2. Dashboard release – what’s available?3. Graduate Outcomes Dashboard demonstration4. What’s next?
Content
What isAnalytics Labs?
Analytics Labs is a unique Continuing Professional Development opportunity for participants from across the UK HE sector offered in a supported data processing environment. Teams with a range of expertise in data and visualisation work together
with sector colleagues with an in-depth knowledge of the policy context. This Agile collaboration rapidly results in the creation of Community
Dashboards by the sector for the sector... 246 participants from 95 UK Universities so far…
What is Analytics Labs?
Analytics Labs - The Approach
Makeup of a team
Product Owner
Brings an understanding of the policy context and the needs of
users
Analysts
Expertise in data and analysis,
especially from a HEI perspective
Scrum Master
Keeps the project on track
and removes impediments to
progress
Data & Viz Support
Supports the team with specialist
knowledge in tools such as Alteryx
and Tableau
Meta Product Owner
Provides expertise and guidance in the
specific theme
Widening Participation (Inclusion)
As a WP Practitioner/School Outreach Activity Officer When deciding to allocate resources on outreach activity I want to Identify areas/schools in which to concentrate outreach activities So I can ultimately, increase recruitment from selected areas/schools and improve social mobility
User Story Category Heat MapStudent Journey Widening
ParticipationBenchmarking Equality Destinations
Post Graduate Library Teaching Excellence and Student Outcomes Framework (TEF)
Marketing Research Assessment Exercise (RAE)
Student Experience
Brexit Estates Accommodation Postgraduate Teaching (PGT)
Postgraduate Research (PGR)
Course Development
Retention Course Management
Course Articulation
Library Usage Quality Assurance Value Added Course Offering Planning
A digital badge records participation against 5 competencies: Participating in Agile development Visualising data Transforming data Digital collaboration Understanding policy and the data
landscape
Analytics Labs - Digital Badge
Community Dashboards – by the sector for the sector
Community Dashboards – can be explored in Heidi Plushttps://www.hesa.ac.uk/services/heidi-plus
ID Name Release Date
1 Athena Swan & Race Equality Dashboard Dec 162 Destination of Leavers by Activity Dec 163 Destination of Leavers Explorer Dec 164 HE-BCI Part B Explorer Dec 165 University Research Benchmarking Feb 186 Finding Comparable Providers Sep 177 League Table Dashboard Jan 189 School Finder Sep 1711 Single HEI Comparison by FTE Sep 1712 Destination Flow Sep 1722 A-Level Subjects Feb 1825 Costs vs Staff Correlation Feb 1829 Brexit Implications on Research Jan 1831 Estates Sector Benchmarks Jan 1839 Provider Healthcheck Apr 18
Community Dashboard portfolio
Community Dashboard BetasID Name Release Date
1 Age & Workforce Planning Jan 182 Destinations Analysis Jan 183 Financial Indicators Jan 184 Future Course Explorer Jan 185 Market Insight Jan 186 TEF Exploratory Dashboard Jan 187 TEF Metrics Core and Split Metrics Jan 18
Dashboard Demos
What’s next?
Developing dashboard suites to address: Course Market Research
KS5 subject analysis Undergraduate course provision Student destinations Industry and workforce analysis
Staff Metrics Recruitment, retention and progression Sickness and absence Workforce planning (including ageing workforce) Staff demographics
Special Projects Team - April to August 2018
About Heidi Plus [email protected]
About Community Dashboards - try them now https://www.jisc.ac.uk/rd/get-involved/try-out-our-community-dashboards
About Analytics Labs - https://www.jisc.ac.uk/rd/projects/business-intelligence-project
Keep in touch: Join our list - www.jiscmail.ac.uk/JISC-HESA-BUSINESS-INTEL Follow our blog - https://businessintelligence.jiscinvolve.org/wp/ Drop us a line at [email protected] entering ‘Analytics Labs' in the subject line
Above details are all available via - https://tinyurl.com/Jisc-BI-Project
Find out more...
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Powering your Voice of the Customer
and Market Research
Programmes
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Offices
Partners
surveys sent in 201712 million page views per day
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250Kuptimein 2017
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staff worldwide
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• Time is short due to busy lives• Surveys fit in around their
lifestyle• Portable rather than fixed
devices• Ever changing mobile and email
addresses• Adopt a stop/start approach to
tasks• Never far from their phones
Challenges of Surveying Graduates
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Reaching GraduatesSolutions That Suit The Graduate
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Reaching GraduatesSolutions That Suit The Graduate
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Mix Mode Challenges
• Over contact• Continuing to request a response from a graduate that has completed
a survey• Updated contact details• Partially completed surveys• Transition from one mode to another• Response rates
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Confirmit Solution
Single contact record• Drives all communication• Determines the frequency of contact• Simplifies update to details• Automatically removed from all contact once completed survey• Easy to remove duplicates
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• These are your graduates and it is important:• Not to over contact• Act upon do not contact
requests• Protect their data
• It is your reputation that HESA is upholding
Managing Communication
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Dual branding of surveys and communications to improve responses
Use best practice to enhance Providers reputation
Class leading technology delivers a seamless experience
Consistent survey and approach ensures a fair and equal comparison between Providers
No Gaming of system!
Results
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Thank You
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Closing remarks
History
• 1972 – First Destinations Record (USR)
• 1984 – Exam Results and First Destinations Supplement (DfE)
• 1993 – First Destinations Supplement (HESA)
• 2002 – Destinations of Leavers from HE (HESA)
• 2017 – Graduate Outcomes
Closing remarks